Predictive scale-bridging simulations through active learning.

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media.

Physical processes that occur within porous materials have wide-ranging applications including - but not limited to - carbon sequestration, battery technology, membranes, oil and gas, geothermal energy, nuclear waste disposal, water resource management. The equations that describe these physical processes have been studied extensively; however, approximating them numerically requires immense computational resources due to the complex behavior that arises from the geometrically-intricate solid boundary conditions in porous materials. Here, we introduce a new dataset of unprecedented scale and breadth, DRP-372: a catalog of 3D geometries, simulation results, and structural properties of samples hosted on the Digital Rocks Portal. The dataset includes 1736 flow and electrical simulation results on 217 samples, which required more than 500 core years of computation. This data can be used for many purposes, such as constructing empirical models, validating new simulation codes, and developing machine learning algorithms that closely match the extensive purely-physical simulation. This article offers a detailed description of the contents of the dataset including the data collection, simulation schemes, and data validation.

Evaluating three biomarkers as prognostic factors of in-hospital mortality and severity in heart failure: A prospective cohort.

OBJECTIVE: To identify the relationship between red blood cell distribution width (RDW, %), interleukin-6 (IL-6) (pg/ml), high sensitivity-c-reactive protein (hs-CRP) (mg/l), in-hospital mortality and disease severity among patients with heart failure (HF). METHODS: Prospective cohort. We included adults diagnosed with acute non-ischemic HF in 2015. The dependent variables were in-hospital mortality (yes or no) and disease severity. The latter was assessed with the Get With The Guidelines-HF score. We used hierarchical regression models to describe the pattern of association between biomarkers, mortality, and severity. We used the Youden index to identify the best cut-off for mortality prediction. RESULTS: We included 167 patients; the mean age was 72.61 (SD: 11.06). The majority of patients presented with New York Heart Association classification II (40.12%) or III (43.11%). After adjusting for age and gender, all biomarkers were associated with mortality. After adding comorbidities, only IL-6 was associated. The final model with all clinical variables showed no effect from any biomarker. The best cut-off for RDW, hs-CRP and IL-6 for mortality were 14.8, 68.7 and 52.9, respectively. IL-6 presented the highest sensitivity (100%), specificity (75.35%) and area under the curve (0.91). CONCLUSIONS: No biomarker is independent from the most important clinical variables; therefore it should not be used for management modifications.

3D Dataset of binary images: A collection of synthetically created digital rock images of complex media.

Digital rock images are computational representations that capture the geometrical complexity of systems present ubiquitously in nature. In recent years, their use has become widespread due to the increasing availability of repositories, and open-source physics simulators and analysis tools. Here, we present a dataset of 3D binary geometries in a standardized format that represent a wide variety of geological and engineering systems. Our data is freely available at [1].

A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells.

Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibroblasts, a highly contractile and secretory phenotype. Myofibroblasts are commonly identified in vitro by the de novo assembly of alpha-smooth muscle actin stress fibers; however, there are few methods to automate stress fiber identification, which can lead to subjectivity and tedium in the process. To address this limitation, we present a computer vision model to classify and segment cells containing alpha-smooth muscle actin stress fibers into 2 classes (α-SMA SF+ and α-SMA SF-), with a high degree of accuracy (cell accuracy: 77%, F1 score 0.79). The model combines standard image processing methods with deep learning techniques to achieve semantic segmentation of the different cell phenotypes. We apply this model to cardiac fibroblasts cultured on hyaluronic acid-based hydrogels of various moduli to induce alpha-smooth muscle actin stress fiber formation. The model successfully predicts the same trends in stress fiber identification as obtained with a manual analysis. Taken together, this work demonstrates a process to automate stress fiber identification in in vitro fibrotic models, thereby increasing reproducibility in fibroblast phenotypic characterization.

Impact of COVID-19 on pacemaker implant.

Objectives: The purpose of this article was to determine the change in the volume of pacemaker implantations with the COVID-2019 pandemic and to assess the change in the number of pacemaker implants according to etiology during the pandemic. Background: The establishment of a mandatory social isolation have generated a decrease in activities in cardiology units. Methods: Descriptive, cross-sectional study that used a database of a Peruvian Hospital. Time was divided into three categories: Before COVID period and COVID period including Previous to Social isolation (SI) and Social Isolation. The number of pacemaker implantations were compared per the same amount of time. Results: A reduction in the pacemaker implant of 73% (95% CI: 33-113; P < .001) was observed during the COVID-19 pandemic period, and a reduction of 78% of patients with the diagnosis of complete or high-grade atrioventricular block and a reduction in the de-novo pacemaker implant was observed, regardless of the etiology. Conclusions: Our results indicate a very significant reduction (73%) in de-novo pacemaker implantation during the months of the COVID-19 pandemic. The reduction in the number of de-novo pacemaker occurred independent of the etiology.